A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presence of low level of contaminants. In this situation pollutant levels are rarely available for every location and time interval visited by the subjects; therefore data gaps need to be filled-in through space-time (ST) interpolation. Surprisingly, there is currently no commercial software for the geostatistical treatment of space-time data, including the interpolation at unmonitored times and locations. This SBIR project is developing the first commercial software to offer tools for geostatistical ST interpolation and modeling of uncertainty. The research product will be a stand-alone module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This software package will offer a comprehensive suite for: 1) the computation and advisor-guided modeling of ST variograms, 2) the ST prediction and stochastic modeling of exposure data at the same scale as health outcome (i.e. aggregated or individual-level) and using any secondary information available (e.g. remote sensing, land-use regression model, air dispersion model, other air pollutants), and 3) the quantification and Monte-Carlo based propagation of uncertainty attached to estimates through exposure reconstruction. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, nuclear environmental engineering or climate change, broadening significantly the commercial market for the end product. This project will accomplish four aims: ? Expand the statistical methodology developed in Phase I to tackle: 1) the case where multiple correlated attributes (e.g. air pollutants) were measured with different sampling densities and temporal frequencies, which will require developing ST cokriging and testing its performance over the kriging approach implemented in Phase I, and 2) stochastic modeling and propagation of exposure uncertainty (exposure measurement errors) through regression analysis. Build a fully functional and tested ST interpolation and simulation module ready for commercial distribution. Conduct a usability study to evaluate the design of the prototype based on NIH usability protocols. Apply the software to demonstrate the approach and its unique benefits in several epidemiological studies, including impact of air pollution on birth outcomes and urban extreme heat on cardiovascular mortality. These technologic, scientific and commercial innovations will revolutionize our ability to model geostatistically space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiological studies.